Overview

Dataset statistics

Number of variables24
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows35
Duplicate rows (%)0.1%
Total size in memory5.5 MiB
Average record size in memory192.0 B

Variable types

Numeric21
Categorical3

Alerts

Dataset has 35 (0.1%) duplicate rowsDuplicates
PAY_0 is highly overall correlated with PAY_2 and 5 other fieldsHigh correlation
PAY_2 is highly overall correlated with PAY_0 and 4 other fieldsHigh correlation
PAY_3 is highly overall correlated with PAY_0 and 4 other fieldsHigh correlation
PAY_4 is highly overall correlated with PAY_0 and 4 other fieldsHigh correlation
PAY_5 is highly overall correlated with PAY_0 and 5 other fieldsHigh correlation
PAY_6 is highly overall correlated with PAY_0 and 5 other fieldsHigh correlation
BILL_AMT1 is highly overall correlated with LIMIT_BAL and 6 other fieldsHigh correlation
BILL_AMT2 is highly overall correlated with LIMIT_BAL and 6 other fieldsHigh correlation
BILL_AMT3 is highly overall correlated with BILL_AMT1 and 6 other fieldsHigh correlation
BILL_AMT4 is highly overall correlated with LIMIT_BAL and 6 other fieldsHigh correlation
BILL_AMT5 is highly overall correlated with LIMIT_BAL and 8 other fieldsHigh correlation
BILL_AMT6 is highly overall correlated with LIMIT_BAL and 6 other fieldsHigh correlation
PAY_AMT1 is highly overall correlated with PAY_AMT2 and 2 other fieldsHigh correlation
PAY_AMT2 is highly overall correlated with BILL_AMT3 and 3 other fieldsHigh correlation
PAY_AMT3 is highly overall correlated with LIMIT_BAL and 8 other fieldsHigh correlation
PAY_AMT4 is highly overall correlated with PAY_AMT1 and 1 other fieldsHigh correlation
PAY_AMT5 is highly overall correlated with BILL_AMT3 and 1 other fieldsHigh correlation
PAY_AMT6 is highly overall correlated with BILL_AMT5 and 4 other fieldsHigh correlation
LIMIT_BAL is highly overall correlated with BILL_AMT1 and 5 other fieldsHigh correlation
default payment next month is highly overall correlated with PAY_0High correlation
PAY_AMT2 is highly skewed (γ1 = 30.45381745)Skewed
PAY_0 has 14737 (49.1%) zerosZeros
PAY_2 has 15730 (52.4%) zerosZeros
PAY_3 has 15764 (52.5%) zerosZeros
PAY_4 has 16455 (54.9%) zerosZeros
PAY_5 has 16947 (56.5%) zerosZeros
PAY_6 has 16286 (54.3%) zerosZeros
BILL_AMT1 has 2008 (6.7%) zerosZeros
BILL_AMT2 has 2506 (8.4%) zerosZeros
BILL_AMT3 has 2870 (9.6%) zerosZeros
BILL_AMT4 has 3195 (10.7%) zerosZeros
BILL_AMT5 has 3506 (11.7%) zerosZeros
BILL_AMT6 has 4020 (13.4%) zerosZeros
PAY_AMT1 has 5249 (17.5%) zerosZeros
PAY_AMT2 has 5396 (18.0%) zerosZeros
PAY_AMT3 has 5968 (19.9%) zerosZeros
PAY_AMT4 has 6408 (21.4%) zerosZeros
PAY_AMT5 has 6703 (22.3%) zerosZeros
PAY_AMT6 has 7173 (23.9%) zerosZeros

Reproduction

Analysis started2023-02-23 14:43:53.409800
Analysis finished2023-02-23 14:44:48.361299
Duration54.95 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ)

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.32
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-23T20:44:48.460530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.66
Coefficient of variation (CV)0.77468541
Kurtosis0.5362629
Mean167484.32
Median Absolute Deviation (MAD)90000
Skewness0.99286696
Sum5.0245297 × 109
Variance1.6834456 × 1010
MonotonicityNot monotonic
2023-02-23T20:44:48.596182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 3365
 
11.2%
20000 1976
 
6.6%
30000 1610
 
5.4%
80000 1567
 
5.2%
200000 1528
 
5.1%
150000 1110
 
3.7%
100000 1048
 
3.5%
180000 995
 
3.3%
360000 881
 
2.9%
60000 825
 
2.8%
Other values (71) 15095
50.3%
ValueCountFrequency (%)
10000 493
 
1.6%
16000 2
 
< 0.1%
20000 1976
6.6%
30000 1610
5.4%
40000 230
 
0.8%
50000 3365
11.2%
60000 825
 
2.8%
70000 731
 
2.4%
80000 1567
5.2%
90000 651
 
2.2%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
800000 2
 
< 0.1%
780000 2
 
< 0.1%
760000 1
 
< 0.1%
750000 4
< 0.1%
740000 2
 
< 0.1%
730000 2
 
< 0.1%
720000 3
 
< 0.1%
710000 6
< 0.1%
700000 8
< 0.1%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
2
18112 
1
11888 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Length

2023-02-23T20:44:48.712870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-23T20:44:48.818814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring characters

ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common 30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

EDUCATION
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8531333
Minimum0
Maximum6
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-23T20:44:48.891619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79034866
Coefficient of variation (CV)0.42649314
Kurtosis2.0786216
Mean1.8531333
Median Absolute Deviation (MAD)1
Skewness0.97097205
Sum55594
Variance0.624651
MonotonicityNot monotonic
2023-02-23T20:44:48.969411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 14030
46.8%
1 10585
35.3%
3 4917
 
16.4%
5 280
 
0.9%
4 123
 
0.4%
6 51
 
0.2%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 10585
35.3%
2 14030
46.8%
3 4917
 
16.4%
4 123
 
0.4%
5 280
 
0.9%
6 51
 
0.2%
ValueCountFrequency (%)
6 51
 
0.2%
5 280
 
0.9%
4 123
 
0.4%
3 4917
 
16.4%
2 14030
46.8%
1 10585
35.3%
0 14
 
< 0.1%

MARRIAGE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
2
15964 
1
13659 
3
 
323
0
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Length

2023-02-23T20:44:49.064158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-23T20:44:49.160899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

AGE
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-23T20:44:49.265619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2179041
Coefficient of variation (CV)0.25976537
Kurtosis0.044303378
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.73224587
Sum1064565
Variance84.969755
MonotonicityNot monotonic
2023-02-23T20:44:49.388292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1605
 
5.3%
27 1477
 
4.9%
28 1409
 
4.7%
30 1395
 
4.7%
26 1256
 
4.2%
31 1217
 
4.1%
25 1186
 
4.0%
34 1162
 
3.9%
32 1158
 
3.9%
33 1146
 
3.8%
Other values (46) 16989
56.6%
ValueCountFrequency (%)
21 67
 
0.2%
22 560
 
1.9%
23 931
3.1%
24 1127
3.8%
25 1186
4.0%
26 1256
4.2%
27 1477
4.9%
28 1409
4.7%
29 1605
5.3%
30 1395
4.7%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 3
 
< 0.1%
74 1
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 10
< 0.1%
69 15
0.1%
68 5
 
< 0.1%
67 16
0.1%

PAY_0
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0167
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.1%
Negative8445
Negative (%)28.1%
Memory size234.5 KiB
2023-02-23T20:44:49.496003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1238015
Coefficient of variation (CV)-67.293505
Kurtosis2.720715
Mean-0.0167
Median Absolute Deviation (MAD)1
Skewness0.73197493
Sum-501
Variance1.2629299
MonotonicityNot monotonic
2023-02-23T20:44:49.583769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14737
49.1%
-1 5686
 
19.0%
1 3688
 
12.3%
-2 2759
 
9.2%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
8 19
 
0.1%
6 11
 
< 0.1%
ValueCountFrequency (%)
-2 2759
 
9.2%
-1 5686
 
19.0%
0 14737
49.1%
1 3688
 
12.3%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
6 11
 
< 0.1%
7 9
 
< 0.1%
ValueCountFrequency (%)
8 19
 
0.1%
7 9
 
< 0.1%
6 11
 
< 0.1%
5 26
 
0.1%
4 76
 
0.3%
3 322
 
1.1%
2 2667
 
8.9%
1 3688
 
12.3%
0 14737
49.1%
-1 5686
 
19.0%

PAY_2
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13376667
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.4%
Negative9832
Negative (%)32.8%
Memory size234.5 KiB
2023-02-23T20:44:49.669539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197186
Coefficient of variation (CV)-8.9498079
Kurtosis1.5704177
Mean-0.13376667
Median Absolute Deviation (MAD)0
Skewness0.79056502
Sum-4013
Variance1.4332543
MonotonicityNot monotonic
2023-02-23T20:44:49.738355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15730
52.4%
-1 6050
 
20.2%
2 3927
 
13.1%
-2 3782
 
12.6%
3 326
 
1.1%
4 99
 
0.3%
1 28
 
0.1%
5 25
 
0.1%
7 20
 
0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
-2 3782
 
12.6%
-1 6050
 
20.2%
0 15730
52.4%
1 28
 
0.1%
2 3927
 
13.1%
3 326
 
1.1%
4 99
 
0.3%
5 25
 
0.1%
6 12
 
< 0.1%
7 20
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 20
 
0.1%
6 12
 
< 0.1%
5 25
 
0.1%
4 99
 
0.3%
3 326
 
1.1%
2 3927
 
13.1%
1 28
 
0.1%
0 15730
52.4%
-1 6050
 
20.2%

PAY_3
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1662
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.5%
Negative10023
Negative (%)33.4%
Memory size234.5 KiB
2023-02-23T20:44:49.820236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1968676
Coefficient of variation (CV)-7.2013692
Kurtosis2.0844359
Mean-0.1662
Median Absolute Deviation (MAD)0
Skewness0.84068183
Sum-4986
Variance1.432492
MonotonicityNot monotonic
2023-02-23T20:44:49.908245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15764
52.5%
-1 5938
 
19.8%
-2 4085
 
13.6%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
-2 4085
 
13.6%
-1 5938
 
19.8%
0 15764
52.5%
1 4
 
< 0.1%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
5 21
 
0.1%
6 23
 
0.1%
7 27
 
0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
4 76
 
0.3%
3 240
 
0.8%
2 3819
 
12.7%
1 4
 
< 0.1%
0 15764
52.5%
-1 5938
 
19.8%

PAY_4
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.22066667
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10035
Negative (%)33.5%
Memory size234.5 KiB
2023-02-23T20:44:49.996794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1691386
Coefficient of variation (CV)-5.2982113
Kurtosis3.4969835
Mean-0.22066667
Median Absolute Deviation (MAD)0
Skewness0.99962941
Sum-6620
Variance1.3668851
MonotonicityNot monotonic
2023-02-23T20:44:50.084220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16455
54.9%
-1 5687
 
19.0%
-2 4348
 
14.5%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
7 58
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
-2 4348
 
14.5%
-1 5687
 
19.0%
0 16455
54.9%
1 2
 
< 0.1%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
7 58
 
0.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 58
 
0.2%
6 5
 
< 0.1%
5 35
 
0.1%
4 69
 
0.2%
3 180
 
0.6%
2 3159
 
10.5%
1 2
 
< 0.1%
0 16455
54.9%
-1 5687
 
19.0%

PAY_5
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2662
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.5%
Negative10085
Negative (%)33.6%
Memory size234.5 KiB
2023-02-23T20:44:50.172260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1331874
Coefficient of variation (CV)-4.2569024
Kurtosis3.9897481
Mean-0.2662
Median Absolute Deviation (MAD)0
Skewness1.008197
Sum-7986
Variance1.2841137
MonotonicityNot monotonic
2023-02-23T20:44:50.259255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
7 58
 
0.2%
5 17
 
0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 4546
 
15.2%
-1 5539
 
18.5%
0 16947
56.5%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
5 17
 
0.1%
6 4
 
< 0.1%
7 58
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 58
 
0.2%
6 4
 
< 0.1%
5 17
 
0.1%
4 84
 
0.3%
3 178
 
0.6%
2 2626
 
8.8%
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%

PAY_6
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2911
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.3%
Negative10635
Negative (%)35.4%
Memory size234.5 KiB
2023-02-23T20:44:50.346247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1499876
Coefficient of variation (CV)-3.95049
Kurtosis3.4265341
Mean-0.2911
Median Absolute Deviation (MAD)0
Skewness0.94802939
Sum-8733
Variance1.3224715
MonotonicityNot monotonic
2023-02-23T20:44:50.415230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 4895
 
16.3%
-1 5740
 
19.1%
0 16286
54.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
5 13
 
< 0.1%
6 19
 
0.1%
7 46
 
0.2%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
4 49
 
0.2%
3 184
 
0.6%
2 2766
 
9.2%
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%

BILL_AMT1
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.331
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2023-02-23T20:44:50.518501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.861
Coefficient of variation (CV)1.4375453
Kurtosis9.8062893
Mean51223.331
Median Absolute Deviation (MAD)21800.5
Skewness2.663861
Sum1.5366999 × 109
Variance5.42224 × 109
MonotonicityNot monotonic
2023-02-23T20:44:50.644263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2008
 
6.7%
390 244
 
0.8%
780 76
 
0.3%
326 72
 
0.2%
316 63
 
0.2%
2500 59
 
0.2%
396 49
 
0.2%
2400 39
 
0.1%
416 29
 
0.1%
500 25
 
0.1%
Other values (22713) 27336
91.1%
ValueCountFrequency (%)
-165580 1
< 0.1%
-154973 1
< 0.1%
-15308 1
< 0.1%
-14386 1
< 0.1%
-11545 1
< 0.1%
-10682 1
< 0.1%
-9802 1
< 0.1%
-9095 1
< 0.1%
-8187 1
< 0.1%
-7438 1
< 0.1%
ValueCountFrequency (%)
964511 1
< 0.1%
746814 1
< 0.1%
653062 1
< 0.1%
630458 1
< 0.1%
626648 1
< 0.1%
621749 1
< 0.1%
613860 1
< 0.1%
610723 1
< 0.1%
608594 1
< 0.1%
604019 1
< 0.1%

BILL_AMT2
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.075
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2023-02-23T20:44:50.756796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.769
Coefficient of variation (CV)1.4472368
Kurtosis10.302946
Mean49179.075
Median Absolute Deviation (MAD)20810
Skewness2.7052209
Sum1.4753723 × 109
Variance5.0657054 × 109
MonotonicityNot monotonic
2023-02-23T20:44:50.886746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2506
 
8.4%
390 231
 
0.8%
326 75
 
0.2%
780 75
 
0.2%
316 72
 
0.2%
396 51
 
0.2%
2500 51
 
0.2%
2400 42
 
0.1%
-200 29
 
0.1%
416 28
 
0.1%
Other values (22336) 26840
89.5%
ValueCountFrequency (%)
-69777 1
< 0.1%
-67526 1
< 0.1%
-33350 1
< 0.1%
-30000 1
< 0.1%
-26214 1
< 0.1%
-24704 1
< 0.1%
-24702 1
< 0.1%
-22960 1
< 0.1%
-18618 1
< 0.1%
-18088 1
< 0.1%
ValueCountFrequency (%)
983931 1
< 0.1%
743970 1
< 0.1%
671563 1
< 0.1%
646770 1
< 0.1%
624475 1
< 0.1%
605943 1
< 0.1%
597793 1
< 0.1%
586825 1
< 0.1%
581775 1
< 0.1%
577681 1
< 0.1%

BILL_AMT3
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.155
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2023-02-23T20:44:51.016770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.387
Coefficient of variation (CV)1.475106
Kurtosis19.783255
Mean47013.155
Median Absolute Deviation (MAD)19708.5
Skewness3.08783
Sum1.4103946 × 109
Variance4.8093375 × 109
MonotonicityNot monotonic
2023-02-23T20:44:51.145266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2870
 
9.6%
390 275
 
0.9%
780 74
 
0.2%
326 63
 
0.2%
316 62
 
0.2%
396 48
 
0.2%
2500 40
 
0.1%
2400 39
 
0.1%
416 29
 
0.1%
200 27
 
0.1%
Other values (22016) 26473
88.2%
ValueCountFrequency (%)
-157264 1
< 0.1%
-61506 1
< 0.1%
-46127 1
< 0.1%
-34041 1
< 0.1%
-25443 1
< 0.1%
-24702 1
< 0.1%
-20320 1
< 0.1%
-17706 1
< 0.1%
-15910 1
< 0.1%
-15641 1
< 0.1%
ValueCountFrequency (%)
1664089 1
< 0.1%
855086 1
< 0.1%
693131 1
< 0.1%
689643 1
< 0.1%
689627 1
< 0.1%
632041 1
< 0.1%
597415 1
< 0.1%
578971 1
< 0.1%
577957 1
< 0.1%
577015 1
< 0.1%

BILL_AMT4
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.949
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2023-02-23T20:44:51.283224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.856
Coefficient of variation (CV)1.4870197
Kurtosis11.309325
Mean43262.949
Median Absolute Deviation (MAD)18656
Skewness2.8219653
Sum1.2978885 × 109
Variance4.1387164 × 109
MonotonicityNot monotonic
2023-02-23T20:44:51.415214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3195
 
10.7%
390 246
 
0.8%
780 101
 
0.3%
316 68
 
0.2%
326 62
 
0.2%
396 44
 
0.1%
2400 39
 
0.1%
150 39
 
0.1%
2500 34
 
0.1%
416 33
 
0.1%
Other values (21538) 26139
87.1%
ValueCountFrequency (%)
-170000 1
< 0.1%
-81334 1
< 0.1%
-65167 1
< 0.1%
-50616 1
< 0.1%
-46627 1
< 0.1%
-34503 1
< 0.1%
-27490 1
< 0.1%
-24303 1
< 0.1%
-22108 1
< 0.1%
-20320 1
< 0.1%
ValueCountFrequency (%)
891586 1
< 0.1%
706864 1
< 0.1%
628699 1
< 0.1%
616836 1
< 0.1%
572805 1
< 0.1%
569034 1
< 0.1%
565669 1
< 0.1%
563543 1
< 0.1%
548020 1
< 0.1%
542653 1
< 0.1%

BILL_AMT5
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.401
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2023-02-23T20:44:51.547308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.156
Coefficient of variation (CV)1.5081876
Kurtosis12.305881
Mean40311.401
Median Absolute Deviation (MAD)17688.5
Skewness2.8763799
Sum1.209342 × 109
Variance3.6962941 × 109
MonotonicityNot monotonic
2023-02-23T20:44:51.671657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3506
 
11.7%
390 235
 
0.8%
780 94
 
0.3%
316 79
 
0.3%
326 62
 
0.2%
150 58
 
0.2%
396 47
 
0.2%
2400 39
 
0.1%
2500 37
 
0.1%
416 36
 
0.1%
Other values (21000) 25807
86.0%
ValueCountFrequency (%)
-81334 1
< 0.1%
-61372 1
< 0.1%
-53007 1
< 0.1%
-46627 1
< 0.1%
-37594 1
< 0.1%
-36156 1
< 0.1%
-30481 1
< 0.1%
-28335 1
< 0.1%
-23003 1
< 0.1%
-20753 1
< 0.1%
ValueCountFrequency (%)
927171 1
< 0.1%
823540 1
< 0.1%
587067 1
< 0.1%
551702 1
< 0.1%
547880 1
< 0.1%
530672 1
< 0.1%
524315 1
< 0.1%
516139 1
< 0.1%
514114 1
< 0.1%
508213 1
< 0.1%

BILL_AMT6
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.76
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2023-02-23T20:44:51.797292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.108
Coefficient of variation (CV)1.5320661
Kurtosis12.270705
Mean38871.76
Median Absolute Deviation (MAD)16755
Skewness2.8466446
Sum1.1661528 × 109
Variance3.5466917 × 109
MonotonicityNot monotonic
2023-02-23T20:44:51.932597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4020
 
13.4%
390 207
 
0.7%
780 86
 
0.3%
150 78
 
0.3%
316 77
 
0.3%
326 56
 
0.2%
396 45
 
0.1%
416 36
 
0.1%
-18 33
 
0.1%
2400 32
 
0.1%
Other values (20594) 25330
84.4%
ValueCountFrequency (%)
-339603 1
< 0.1%
-209051 1
< 0.1%
-150953 1
< 0.1%
-94625 1
< 0.1%
-73895 1
< 0.1%
-57060 1
< 0.1%
-51443 1
< 0.1%
-51183 1
< 0.1%
-46627 1
< 0.1%
-45734 1
< 0.1%
ValueCountFrequency (%)
961664 1
< 0.1%
699944 1
< 0.1%
568638 1
< 0.1%
527711 1
< 0.1%
527566 1
< 0.1%
514975 1
< 0.1%
513798 1
< 0.1%
511905 1
< 0.1%
501370 1
< 0.1%
499100 1
< 0.1%

PAY_AMT1
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-23T20:44:52.069208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28
Coefficient of variation (CV)2.9245246
Kurtosis415.25474
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.668364
Sum1.6990742 × 108
Variance2.7434226 × 108
MonotonicityNot monotonic
2023-02-23T20:44:52.187477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5249
 
17.5%
2000 1363
 
4.5%
3000 891
 
3.0%
5000 698
 
2.3%
1500 507
 
1.7%
4000 426
 
1.4%
10000 401
 
1.3%
1000 365
 
1.2%
2500 298
 
1.0%
6000 294
 
1.0%
Other values (7933) 19508
65.0%
ValueCountFrequency (%)
0 5249
17.5%
1 9
 
< 0.1%
2 14
 
< 0.1%
3 15
 
0.1%
4 18
 
0.1%
5 12
 
< 0.1%
6 15
 
0.1%
7 9
 
< 0.1%
8 8
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
873552 1
< 0.1%
505000 1
< 0.1%
493358 1
< 0.1%
423903 1
< 0.1%
405016 1
< 0.1%
368199 1
< 0.1%
323014 1
< 0.1%
304815 1
< 0.1%
302000 1
< 0.1%
300039 1
< 0.1%

PAY_AMT2
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-23T20:44:52.311076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.87
Coefficient of variation (CV)3.8912741
Kurtosis1641.6319
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.453817
Sum1.776349 × 108
Variance5.3088171 × 108
MonotonicityNot monotonic
2023-02-23T20:44:52.436699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5396
 
18.0%
2000 1290
 
4.3%
3000 857
 
2.9%
5000 717
 
2.4%
1000 594
 
2.0%
1500 521
 
1.7%
4000 410
 
1.4%
10000 318
 
1.1%
6000 283
 
0.9%
2500 251
 
0.8%
Other values (7889) 19363
64.5%
ValueCountFrequency (%)
0 5396
18.0%
1 15
 
0.1%
2 20
 
0.1%
3 18
 
0.1%
4 11
 
< 0.1%
5 25
 
0.1%
6 8
 
< 0.1%
7 12
 
< 0.1%
8 9
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
1684259 1
< 0.1%
1227082 1
< 0.1%
1215471 1
< 0.1%
1024516 1
< 0.1%
580464 1
< 0.1%
415552 1
< 0.1%
401003 1
< 0.1%
388126 1
< 0.1%
385228 1
< 0.1%
384986 1
< 0.1%

PAY_AMT3
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-23T20:44:52.571351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.961
Coefficient of variation (CV)3.3693139
Kurtosis564.31123
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.216635
Sum1.5677044 × 108
Variance3.1000509 × 108
MonotonicityNot monotonic
2023-02-23T20:44:52.688174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5968
 
19.9%
2000 1285
 
4.3%
1000 1103
 
3.7%
3000 870
 
2.9%
5000 721
 
2.4%
1500 490
 
1.6%
4000 381
 
1.3%
10000 312
 
1.0%
1200 243
 
0.8%
6000 241
 
0.8%
Other values (7508) 18386
61.3%
ValueCountFrequency (%)
0 5968
19.9%
1 13
 
< 0.1%
2 19
 
0.1%
3 14
 
< 0.1%
4 15
 
0.1%
5 18
 
0.1%
6 14
 
< 0.1%
7 18
 
0.1%
8 10
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
896040 1
< 0.1%
889043 1
< 0.1%
508229 1
< 0.1%
417588 1
< 0.1%
400972 1
< 0.1%
397092 1
< 0.1%
380478 1
< 0.1%
371718 1
< 0.1%
349395 1
< 0.1%
344261 1
< 0.1%

PAY_AMT4
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.0769
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-23T20:44:52.810196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.16
Coefficient of variation (CV)3.246148
Kurtosis277.33377
Mean4826.0769
Median Absolute Deviation (MAD)1500
Skewness12.904985
Sum1.4478231 × 108
Variance2.4542856 × 108
MonotonicityNot monotonic
2023-02-23T20:44:52.930285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6408
 
21.4%
1000 1394
 
4.6%
2000 1214
 
4.0%
3000 887
 
3.0%
5000 810
 
2.7%
1500 441
 
1.5%
4000 402
 
1.3%
10000 341
 
1.1%
2500 259
 
0.9%
500 258
 
0.9%
Other values (6927) 17586
58.6%
ValueCountFrequency (%)
0 6408
21.4%
1 22
 
0.1%
2 22
 
0.1%
3 13
 
< 0.1%
4 20
 
0.1%
5 12
 
< 0.1%
6 16
 
0.1%
7 11
 
< 0.1%
8 7
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
621000 1
< 0.1%
528897 1
< 0.1%
497000 1
< 0.1%
432130 1
< 0.1%
400046 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
320008 1
< 0.1%
313094 1
< 0.1%
292962 1
< 0.1%

PAY_AMT5
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.3876
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-23T20:44:53.067949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.306
Coefficient of variation (CV)3.1833865
Kurtosis180.06394
Mean4799.3876
Median Absolute Deviation (MAD)1500
Skewness11.127417
Sum1.4398163 × 108
Variance2.3342662 × 108
MonotonicityNot monotonic
2023-02-23T20:44:53.189620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6703
 
22.3%
1000 1340
 
4.5%
2000 1323
 
4.4%
3000 947
 
3.2%
5000 814
 
2.7%
1500 426
 
1.4%
4000 401
 
1.3%
10000 343
 
1.1%
500 250
 
0.8%
6000 247
 
0.8%
Other values (6887) 17206
57.4%
ValueCountFrequency (%)
0 6703
22.3%
1 21
 
0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 12
 
< 0.1%
5 9
 
< 0.1%
6 7
 
< 0.1%
7 9
 
< 0.1%
8 6
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
426529 1
< 0.1%
417990 1
< 0.1%
388071 1
< 0.1%
379267 1
< 0.1%
332000 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
326889 1
< 0.1%
317077 1
< 0.1%
310135 1
< 0.1%

PAY_AMT6
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.5026
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-23T20:44:53.310297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.466
Coefficient of variation (CV)3.4085815
Kurtosis167.16143
Mean5215.5026
Median Absolute Deviation (MAD)1500
Skewness10.640727
Sum1.5646508 × 108
Variance3.1603829 × 108
MonotonicityNot monotonic
2023-02-23T20:44:53.438215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7173
23.9%
1000 1299
 
4.3%
2000 1295
 
4.3%
3000 914
 
3.0%
5000 808
 
2.7%
1500 439
 
1.5%
4000 411
 
1.4%
10000 356
 
1.2%
500 247
 
0.8%
6000 220
 
0.7%
Other values (6929) 16838
56.1%
ValueCountFrequency (%)
0 7173
23.9%
1 20
 
0.1%
2 9
 
< 0.1%
3 14
 
< 0.1%
4 12
 
< 0.1%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 6
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
528666 1
< 0.1%
527143 1
< 0.1%
443001 1
< 0.1%
422000 1
< 0.1%
403500 1
< 0.1%
377000 1
< 0.1%
372495 1
< 0.1%
351282 1
< 0.1%
345293 1
< 0.1%
308000 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Length

2023-02-23T20:44:53.548172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-23T20:44:53.636935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Interactions

2023-02-23T20:44:45.475400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:00.391812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:02.584427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:04.977825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:07.141504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:09.245921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:11.377742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:13.747178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:15.788756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:17.862570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:20.029264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:22.287869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:24.851488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:27.162870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:29.368763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:31.442757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:33.755229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:35.937671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:38.312409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:40.956804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:43.274883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:45.584105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:00.506513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:02.689147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:05.083641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:07.244200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:09.347643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:11.456531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:13.849904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:15.890480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:17.966326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:20.138971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:22.392576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:24.963756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:27.268652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:29.449552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:31.553495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:33.856957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:36.052368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:38.844985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:41.066481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:43.385558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:45.695807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:00.613219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:02.795221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:05.188931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:07.344960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:09.451365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:11.557299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:13.952630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:15.994202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:18.068089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:20.248669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:22.499287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:25.077024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:27.374363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:29.549326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:31.666764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:33.959653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:36.167032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:38.951671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:41.181175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:43.493331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:45.805488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:00.721900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:02.900939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:05.295110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:07.446689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:09.554099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:11.660596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:14.056357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:16.096928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:18.171808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:20.359372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:22.606571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:25.189037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:27.480081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:29.652079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:31.778031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:34.063406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:36.283750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:39.057409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:41.294900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:43.598588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:45.912232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:00.824654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:03.231422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:05.397502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:07.546422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:09.652488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:11.759891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:14.154090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:16.196661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:18.271572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:20.465086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:22.708333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:25.295754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:27.582805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:29.729870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:31.886435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:34.161143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:36.395420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:39.159144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:41.403610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:43.700828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:46.016952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:00.928377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:03.332892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:05.498235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:07.645157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:09.751223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:11.858195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:14.252826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:16.295398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:18.372299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:20.569804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:22.811090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:25.404609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:27.684534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:29.818672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:31.995149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:34.260877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:36.504815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:39.260872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:41.511360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:43.803524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:46.123637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:01.031102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:03.417670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:05.600527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:07.744894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:09.848995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:11.957498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:14.351560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:16.394168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:18.473036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:20.674526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:22.913815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:25.512321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:27.787230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-23T20:44:10.871028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:13.237801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:15.272464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:17.351931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:19.512883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:21.749292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:24.313718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:26.612788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:28.844167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:30.926502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:33.208073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:35.396550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:37.743506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:40.428183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:42.722726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:44.952546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:47.329125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:02.170535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:04.567317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:06.762892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:08.843994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:10.980734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:13.346543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:15.381273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:17.460638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:19.622591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:21.862991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:24.428516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:26.728888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:28.956865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:31.036207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:33.323762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:35.517253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:37.864194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:40.542918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:42.841413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-23T20:44:47.430881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:02.271271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:04.667622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:06.862251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-23T20:44:13.443850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:15.479550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:17.558383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:19.721421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:21.966708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-23T20:44:29.056599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:31.134517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:33.429513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-23T20:44:42.947128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:45.164062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:47.541558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:02.378976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:04.774362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-23T20:44:29.164310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-23T20:44:11.280004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:13.645452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:15.687029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:17.760843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:19.925974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:22.180140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:24.744735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:27.053124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:29.266038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:31.340037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:33.646525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:35.831387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:38.200112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:40.852055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:43.166143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-23T20:44:45.368588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-02-23T20:44:53.745325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-02-23T20:44:53.991672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-23T20:44:54.764465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-23T20:44:55.000125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-23T20:44:55.206575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-02-23T20:44:55.332212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-23T20:44:47.820307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-23T20:44:48.176244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
0200002212422-1-1-2-239133102689000068900001
112000022226-1200022682172526823272345532610100010001000020001
290000222340000002923914027135591433114948155491518150010001000100050000
350000221370000004699048233492912831428959295472000201912001100106910000
45000012157-10-100086175670358352094019146191312000366811000090006896790
5500001123700000064400570695760819394196192002425001815657100010008000
6500000112290000003679654120234450075426534830034739445500040000380002023913750137700
7100000222230-1-100-111876380601221-1595673806010581168715420
81400002312800200011285140961210812211117933719332904321000100010000
92000013235-2-2-2-2-1-10000130071391200013007112200
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
299901400001214100000013832513714213911013826249675461216000700042281505200020000
29991210000121343222222500250025002500250025000000001
299921000013143000-2-2-288021040000002000000000
29993100000112380-1-100030421427102996706266947355004200011178440003000200020000
2999480000122342222227255777708793847751982607811587000350007000040001
299952200001313900000018894819281520836588004312371598085002000050033047500010000
2999615000013243-1-1-1-100168318283502897951900183735268998129000
299973000012237432-10035653356275820878205821935700220004200200031001
2999880000131411-1000-1-16457837976304527741185548944859003409117819265296418041
2999950000121460000004792948905497643653532428153132078180014301000100010001

Duplicate rows

Most frequently occurring

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month# duplicates
0200001222422444416501650165016501650165000000012
150000122261-2-2-2-2-200000000000002
250000212231-2-2-2-2-200000000000002
38000022131-2-2-2-2-2-200000000000002
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58000023142-2-2-2-2-2-200000000000002
690000212311-2-2-2-2-200000000000002
7100000221491-2-2-2-2-200000000000002
8110000212311-2-2-2-2-200000000000002
9140000112291-2-2-2-2-200000000000002